To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors.

Abstract

We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions.

Author Summary

Gene expression is controlled by various gene regulatory factors.

Author Summary

Corruptions of regulatory cooperativity may lead to abnormal gene expression activity such as cancer.

Introduction

Gene eXpression is a compleX process that achieves both spatial and temporal control through the coordinated action of multiple regulatory factors (RFs) [1—3].

Introduction

These regulatory factors affecting gene eXpression take several forms, such as transcription factors (TFs), which directly or indirectly bind DNA at promoter and enhancer regions of their target genes, and non-coding RNAs (e.g.

Introduction

RFs can act as activators or repressors, but ultimately, the target gene eXpression is determined by combining the effects of multiple regulatory factors.

In this study, we use a mathematical model of Dorsal dynamics, fit to experimental data, to determine the ability of the Dorsal gradient to regulate gene expression across the entire dor-sal-ventral axis.

Abstract

We found that two assumptions are required for the model to match experimental data in both Dorsal distribution and gene expression patterns.

Abstract

Our model explains the dynamic behavior of the Dorsal gradient at lateral and dorsal positions of the embryo, the ability of Dorsal to regulate gene expression across the entire dorsal-ventral axis, and the robustness of gene expression to stochastic effects.

Author Summary

Using a mathematical model of the Drosophila embryo, we have proposed a solution to this outstanding problem: namely that Cactus, the inhibitor to Dorsal, is present with Dorsal in nuclei across the embryo, which creates a disparity between the gradient measured by fluorescence and the gradient measured by gene expression .

Gene expression model

Gene expression model

Introduction

Signaling through Toll receptors on the ventral side of the embryo causes the dissociation of the dl/Cact complex, and free dl accumulates in the ventral nuclei [5—7] to create a spatial gradient that causes differential gene expression based on multiple gene expression thresholds.

Introduction

In recent years, detailed measurements of the dl gradient have been performed, potentially allowing us to address the question of how the spatial information carried by the dl gradient results in gene expression [10—12].

Introduction

These observations left open the question of how a narrow-width dl gradient can specify gene expression domains far beyond its apparent spatial range [10, 12, 16].

Optimization

A similar method is used to find parameter sets for simulations of gene eXpression , with l = 250, [,4 = 50.

Optimization

We use the dl/Cact dynamics associated with this set of parameters as an input to the gene eXpression model equations, and allow only the gene eXpression parameters to evolve for 9 different values of the noise parameter, 1], between 0.02 and 0.5.

The 175 ORFs include genes expressing key components of cell cycle progression and regulation: TUBZ and TUB3 encoding a and fl tubulins, CLB4 and PH 080 encoding cyclins, CDC53 and APC9 encoding respectively the cullin structural protein of SCF complexes and a subunit of the Ana-phase-Promoting Complex/Cyclosome; moreover, AME] , RAD24, RAD59 and SWEI involved in checkpoint maintenance, the F U83, DI G2 and SLT2 encoding MAP-kinases and their regulator BMH1 encoding the major isoform of 14-3-3 proteins.

Introduction

The connection between flexibility peaks and ORFs could be the evolutionary outcome of modified canonical polyadenylation elements, leading to a differentiated 3’ end processing and gene expression regulation.

These experiments generated a large data matrix with rows corresponding to gene expression values, and columns corresponding to shRNA perturbations.

Author Summary

PEACS uses a novel computational approach to analyze gene eXpression data from perturbed cellular populations, and can be applied broadly to identify regulators of stem and progenitor cell self-renewal or differentiation.

PEACS: Algorithm

Thus the gene expression data for each perturbation p is mapped into the space spanned by linear combinations of the first k gene-expression SVD eigenvectors 1/1,.

PEACS: Expression Profiling by qPCR

Microfluidic qPCR was carried out according to the manufacturer’s Protocol (Protocol 37: Fast Gene Expression Analysis Using EvaGreen on the BioMark or BioMark HD System).

PEACS: Expression Profiling by qPCR

For the idealized experiment, gene expression was profiled using standard qPCR and the 17 genes profiled were randomly selected transcription factors expressed by MCFIOA cells and implicated in differentiation.

Results

Lastly, we applied SVD, NMF and ICA to the gene expression matrix to assess the relative performance of these algorithms in identifying changes in cell-state proportions.

Results

We could directly compare gene loadings in the various components with gene expression in the various states because the gene-expression profiles of the pure states were known in our idealized experimental conditions (Fig 2E).

The FANTOM5 project has recently produced the most comprehensive expression atlas for human and mouse cells, based upon cap analysis of gene expression (CAGE) data [18].

Kinetics and chromatin features underlying IEG induction

The timing of immediate early and nucleotide binding gene expression is shown in Fig 2A and 2B where it can be seen that in AoSMC-FGFZ, AoSM-C-Ile and MCF7-EGF data the largest proportion of known IEGs is found in the 30-90 min interval when ts values are binned in 30 min intervals (the proportion of clusters annotated to known IEGs is expressed as a percentage of all clusters within each 30 min period according to 1‘5).

Results

These cues are sensed by these cells through changes in imme-diate-early gene expression , and can lead to increased proliferation and migration.

The early peak signature is enriched for lEGs and signalling pathways

Terms relevant to the immediate-early response included regulation of gene expression , regulation of transcription from RNA polymerase II promoter, regulation of RNA metabolic process and regulation of metabolic process.